| /* |
| * Licensed to the Apache Software Foundation (ASF) under one or more |
| * contributor license agreements. See the NOTICE file distributed with |
| * this work for additional information regarding copyright ownership. |
| * The ASF licenses this file to You under the Apache License, Version 2.0 |
| * (the "License"); you may not use this file except in compliance with |
| * the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| package opennlp.tools.ml.maxent.quasinewton; |
| |
| import org.junit.Assert; |
| import org.junit.Test; |
| |
| public class QNMinimizerTest { |
| |
| @Test |
| public void testQuadraticFunction() { |
| QNMinimizer minimizer = new QNMinimizer(); |
| Function f = new QuadraticFunction(); |
| double[] x = minimizer.minimize(f); |
| double minValue = f.valueAt(x); |
| |
| Assert.assertEquals(x[0], 1.0, 1e-5); |
| Assert.assertEquals(x[1], 5.0, 1e-5); |
| Assert.assertEquals(minValue, 10.0, 1e-10); |
| } |
| |
| @Test |
| public void testRosenbrockFunction() { |
| QNMinimizer minimizer = new QNMinimizer(); |
| Function f = new Rosenbrock(); |
| double[] x = minimizer.minimize(f); |
| double minValue = f.valueAt(x); |
| |
| Assert.assertEquals(x[0], 1.0, 1e-5); |
| Assert.assertEquals(x[1], 1.0, 1e-5); |
| Assert.assertEquals(minValue, 0, 1e-10); |
| } |
| |
| /** |
| * Quadratic function: f(x,y) = (x-1)^2 + (y-5)^2 + 10 |
| */ |
| public class QuadraticFunction implements Function { |
| |
| @Override |
| public int getDimension() { |
| return 2; |
| } |
| |
| @Override |
| public double valueAt(double[] x) { |
| return StrictMath.pow(x[0] - 1, 2) + StrictMath.pow(x[1] - 5, 2) + 10; |
| } |
| |
| @Override |
| public double[] gradientAt(double[] x) { |
| return new double[] { 2 * (x[0] - 1), 2 * (x[1] - 5) }; |
| } |
| } |
| |
| /** |
| * Rosenbrock function (http://en.wikipedia.org/wiki/Rosenbrock_function) |
| * f(x,y) = (1-x)^2 + 100*(y-x^2)^2 |
| * f(x,y) is non-convex and has global minimum at (x,y) = (1,1) where f(x,y) = 0 |
| * |
| * f_x = -2*(1-x) - 400*(y-x^2)*x |
| * f_y = 200*(y-x^2) |
| */ |
| public class Rosenbrock implements Function { |
| |
| @Override |
| public int getDimension() { |
| return 2; |
| } |
| |
| @Override |
| public double valueAt(double[] x) { |
| return StrictMath.pow(1 - x[0], 2) + 100 * StrictMath.pow(x[1] - StrictMath.pow(x[0], 2), 2); |
| } |
| |
| @Override |
| public double[] gradientAt(double[] x) { |
| double[] g = new double[2]; |
| g[0] = -2 * (1 - x[0]) - 400 * (x[1] - StrictMath.pow(x[0], 2)) * x[0]; |
| g[1] = 200 * (x[1] - StrictMath.pow(x[0], 2)); |
| return g; |
| } |
| |
| } |
| } |